The Bubble Map Chart

A bubble map chart is a data visualization tool that’s exactly what it sounds like – a combination of a bubble chart and a map chart. A bubble chart is best used to compare data points with three dimensions by the use of positioning and proportions, and a map chart is best used to show data values based on location. Combining the two, the bubble map chart visualizes the distribution and relationships between data points based on size proportion and geographical location.

Just like a map chart, the structure of the bubble map chart is based on the map of a location of interest (a city, state, country, etc.). From there, data points are plotted as bubbles to their corresponding location on the map, often using latitude and longitude coordinates. Like a bubble chart, the size of the bubble is dependent on a third numerical data value with the idea being the larger the value, the larger the bubble.

When to Use a Bubble Map Chart

As mentioned above, bubble map charts should be used when wanting to visualize data based on location and make comparisons based on size and not exact value. With that being said, bubble map charts should only be used when the data you’re working with has a geographical element. It’s also important that your data have a numerical dimension that will correlate to the size of the bubble. Without these two key data characteristics, it’s simply not possible to use a bubble map chart as a visualization.

In a more general sense, bubble map charts are especially useful when knowing specific data values is not of interest, but instead showing where data is located and how the locations relate to one another. A bubble map chart identifies specific locations with corresponding data, and uses the visual element of size to display differences in the magnitude of a certain discrete variable among the locations.

It displays a more generalized view of multi-dimensional data, and is very effective when dealing with larger quantities of data because sizes are often easier to distinguish between and make sense of than raw numbers. Ultimately, bubble map charts are useful in identifying relationships between locations and highlighting clusters of locations with high or low numerical values.

Let’s take a look at an example where the bubble map chart is a great choice of visualization:

Chart made using Chartio

In this example, we’re interested in looking at the relative number of reported UFO sightings per state in the United States for the year 2018. The map clearly represents the United States and the bubbles represent the reported number of sightings in each state. The size of the bubble, more specifically, represents the reported number of sightings on a qualitative spectrum where the larger the bubble, the greater the number of reported sightings. Notice that there isn’t a legend accompanying this chart– a legend is only needed for a bubble map chart when multiple series of data are shown in the graph (i.e. population and number of sightings) and are represented by different colored bubbles.

From the bubble map chart, it’s easy to see that California has the largest number of reported UFO sightings of all 50 states in 2018; we know this because it has the largest size bubble on the entire map. Other high reported UFO sightings, indicated by larger size bubbles, include Washington, Florida, Texas, and New York. It’s also easy to see which states had few reported UFO sightings (North Dakota, Wyoming, etc.) based on the smaller sized bubbles. Identifying relationships between states is as simple as comparing the size of their bubbles, though that can become difficult when bubble sizes are too similar to differentiate between. Despite not knowing the exact number of UFO sightings for each state, the bubble map chart gives us a general idea and highlights key points of the data.

When NOT to Use a Bubble Map Chart

Determining when to use a bubble map chart is usually simple, but there are some areas where a bubble map chart is not the ideal visualization choice. First, bubble map charts should not be used to look at individual data values. Instead, they rely on bubble size to communicate generalities of data values, and it’s not possible to extract individual values from the chart unless a value is included in each bubble or location, or a chart legend is included and very detailed.

Second, bubble map charts should not be used to generalize multiple variables with similar data values. Though we said previously that bubble map charts are great for working with large amounts of data, an issue arises when many of data points have the same or similar numerical values. Because sizes are used to represent numerical values in a bubble map chart, having too many similar values will result in a chart with bubbles of the same or similar size, making them practically non differentiable between one another. This issue makes interpretation of the chart difficult and diminishes the overall purpose.

It’s important to note that having a large number of data points, especially for one location or locations in a tight area, can lead to overcrowding. Often times, this overcrowding causes bubbles to overlap which can lead to an area on the map looking like a mass of color. While overcrowding highlights areas that are dense with data, it also makes it hard to identify bubbles for individual areas and even determine the size of those bubbles. Depending on the purpose of the chart and the information you’re wanting to gain, overcrowding might not be an issue.

Let’s take a look at an example where the bubble map chart is NOT a great choice of visualization:

Chart made using Chartio

This example is very similar to the previous one– we’re still interested in looking at the relative number of reported UFO sightings per state in the United States, but this time we’re only looking at the month of February in 2018. The map still represents the United States, the bubbles still represent the reported number of sightings in each state, and the size of the bubble still represents the reported number of sightings on a qualitative spectrum. Like the previous chart, there isn’t a legend because this chart only displays one series of data.

So why is the bubble map chart a poor choice of visualization for this data? All of the bubbles on the map are the same size; the data values represented in this chart are simply too close or are the same number. Because of this, the only concrete information we can gain from this chart is that in February of 2018, all of the states had a similar small number of reported UFO sightings.

Even if one state had one or two more sightings than all of the others, the change in the size of the bubble would be very minimal and possibly hard to detect among all of the other bubbles. Overall, the similarity in size between all of the bubbles makes it difficult to distinguish between the number of reported sightings for each state and detracts from being able to make identify relationships and comparisons between them.

Comparison of Distribution Chart Types

Simply put, the bubble map chart is a data visualization that’s used to show distributions and relationships of multiple values. Other types of visualizations that show the distribution of multiple values are the map and heat map charts. The table below gives the use case and pros and cons of each distribution chart type:

Bubble Map Chart

Map Chart

Heat Map Chart

Use

Visualize data through sized circles, or bubbles, by geographical location

Visualize data through color variations by geographical location

Visualize data through color variations in a tabular format

Pros

Can display large amounts of information

Easily shows how data breaks down regionally

Can compare proportions over geographic regions

Can display large amounts of information

Color variation can clearly depict relationships between data points and help to draw conclusions about trends

Easily shows how data breaks down regionally

Can display large amounts of information

Color variation can clearly depict relationships between data points and help to draw conclusions about trends

Simple tabular format

Cons

Showing exact values can be difficult, better for relative data

Can become overcrowded easily

Graduations in color are not as effective for discerning subtle differences

Showing exact values can be difficult, better for relative data

Graduations in color are not as effective for discerning subtle differences

Showing exact values can be difficult, better for relative data

References

About Bryn Burns

Hi! I'm Bryn Burns. I am a current senior at Virginia Tech pursuing degrees in Statistics and Mathematics. Data science and visualization are two things I'm very passionate about, as well as working with numbers and helping people learn. I'm thrilled to share my knowledge here at The Data School!